This report provides a comprehensive analysis of global child mortality rates for ages 5-9, using recent UNICEF data. Through a series of visualizations, we explore how mortality rates have changed over time, how they differ by country and region, and what factors may contribute to these disparities. The report also examines the relationship between economic development and child mortality, as well as differences by gender, to highlight areas where targeted interventions are most needed.
Key Findings
Child mortality rates show substantial variation across countries and regions, with some countries experiencing rates several times higher than others.
While global mortality rates have declined over recent decades, progress has been uneven, and significant disparities persist, especially in low-income regions.
Socioeconomic status, healthcare access, and education are strongly associated with lower child mortality rates, as shown by the negative correlation between GDP per capita and mortality.
Gender differences in child mortality are minimal at the global level, but continued monitoring is important to ensure equity.
Trend Analysis
Code
import pandas as pdimport plotly.express as pximport numpy as npfrom sklearn.linear_model import LinearRegression# Load datachild = pd.read_csv('unicef_indicator_1.csv')meta = pd.read_csv('unicef_metadata.csv')# Get the most recent GDP per capita for each countrymeta_gdp = meta.dropna(subset=["GDP per capita (constant 2015 US$)"])meta_gdp = meta_gdp.sort_values("year").groupby("country", as_index=False).last()# Filter for 2022, totalchild_2022 = child[(child['time_period'] ==2022) & (child['sex'] =='Total')]# Merge with most recent GDP per capitamerged = pd.merge(child_2022, meta_gdp[['country', 'GDP per capita (constant 2015 US$)']], on='country', how='left')merged = merged.dropna(subset=['GDP per capita (constant 2015 US$)', 'obs_value'])# Remove zero or negative GDP valuesmerged = merged[merged['GDP per capita (constant 2015 US$)'] >0]# Take log of GDP per capitamerged['log_gdp'] = np.log10(merged['GDP per capita (constant 2015 US$)'])# Prepare regressionX = merged['log_gdp'].values.reshape(-1, 1)y = merged['obs_value'].valuesmodel = LinearRegression()model.fit(X, y)y_pred = model.predict(X)# Scatter plot with regression linefig_gdp = px.scatter( merged, x='log_gdp', y='obs_value', hover_name='country', title='Child Mortality Rates by Log(GDP per Capita) (2022)', labels={'log_gdp': 'Log10(Most Recent GDP per Capita, constant 2015 US$)','obs_value': 'Mortality Rate (per 1000 children)' })# Add regression linefig_gdp.add_traces(px.line( x=merged['log_gdp'], y=y_pred, labels={'x': 'Log10(Most Recent GDP per Capita, constant 2015 US$)', 'y': 'Predicted Mortality Rate'}).data)fig_gdp.update_traces(line=dict(color='red'), selector=dict(type='scatter', mode='lines'))fig_gdp.update_layout( showlegend=False, height=500, margin=dict(l=40, r=40, t=60, b=60), plot_bgcolor='white', paper_bgcolor='white')fig_gdp.show()
Analysis:
The scatterplot demonstrates a clear negative correlation between a country’s economic prosperity (as measured by GDP per capita) and its child mortality rate. Countries with higher GDP per capita tend to have much lower mortality rates, indicating that economic development is a key factor in improving child health outcomes. The log scale on the x-axis highlights that the greatest reductions in mortality are seen as countries move from low to middle income. However, some countries deviate from this trend, suggesting that other factors, such as healthcare quality, conflict, or policy, also play important roles. This visualization underscores the importance of both economic growth and targeted health interventions.
The time series line chart reveals a steady and substantial decline in global child mortality rates for ages 5-9 over the past several decades. This downward trend reflects major improvements in public health, nutrition, disease prevention, and access to medical care worldwide. While the overall pattern is positive, the chart also shows periods of slower progress and occasional plateaus, which may correspond to global crises or regional setbacks. Sustaining and accelerating this progress will require continued investment and attention, especially in countries where declines have stalled.
Global and Regional Comparison of Averages of Child Mortality Rates
The world map visualization highlights stark geographic disparities in child mortality rates. Sub-Saharan African countries are shown in the darkest shades, indicating the highest mortality rates, while countries in Europe, North America, and parts of Asia have the lowest rates. This pattern reflects differences in healthcare infrastructure, economic development, and social stability. The map also reveals that some countries have made significant progress, while others continue to face persistent challenges. These findings emphasize the need for region-specific strategies and international support to address the most affected areas.
Gender Disparities in Averages of Global Child Mortality Rate
Code
import pandas as pdimport plotly.express as px# Load datachild = pd.read_csv('unicef_indicator_1.csv')latest = child[child['time_period'] ==2022]# Only keep Male and Femaleavg_by_gender = latest[latest['sex'].isin(['Male', 'Female'])].groupby('sex', as_index=False)['obs_value'].mean()# Gender-oriented color palettegender_colors = ['#1f77b4', '#e377c2'] # blue for Male, pink for Femalefig_gender = px.bar( avg_by_gender, x='sex', y='obs_value', color='sex', text=avg_by_gender['obs_value'].round(2), title='Child Mortality Rates by Gender (2022)', labels={'obs_value': 'Average Mortality Rate (per 1000 children)', 'sex': 'Gender'}, color_discrete_sequence=gender_colors)fig_gender.update_traces(textposition='outside')fig_gender.update_layout( showlegend=False, height=500, margin=dict(l=40, r=40, t=60, b=60), plot_bgcolor='white', paper_bgcolor='white')fig_gender.show()
Analysis:
The bar chart compares average child mortality rates for boys and girls in 2022. The results show that the rates are nearly identical, with only a very slight difference between genders. This suggests that, at the global level, gender-based disparities in child mortality for ages 5-9 are minimal. However, it is important to note that this global average may mask differences within specific countries or regions, where cultural, social, or economic factors could lead to greater disparities. Ongoing monitoring and disaggregated data are essential to ensure that both boys and girls have equal opportunities for survival and health.
Conclusion
This report demonstrates that while global child mortality rates have declined, significant disparities remain across countries, regions, and genders. The visualizations underscore the importance of economic development, healthcare access, and targeted interventions in reducing mortality. Continued investment in healthcare, education, and region-specific policies is essential to ensure all children have the opportunity to survive and thrive, regardless of where they are born or their gender.
References
UNICEF. (2022). Global Mortality Rates Ages 5-9 for 2022. Retrieved from UNICEF website
World Health Organization (WHO). (2022). Child Mortality. Retrieved from WHO website
United Nations Children’s Fund (UNICEF). (2022). The State of the World’s Children 2022. Retrieved from UNICEF website
United Nations Development Programme (UNDP). (2022). Human Development Report 2022. Retrieved from UNDP website
World Bank. (2022). World Development Indicators. Retrieved from World Bank website
UNICEF. (2022). Child Mortality: A Global Perspective. Retrieved from UNICEF website
Source Code
---title: "Global Mortality Rates: Children Ages 5-9"author: "Megan Burriesce"format: html: embed-resources: true code-fold: true theme: cosmo toc: true toc-location: left code-tools: true code-overflow: wrap code-line-numbers: true code-copy: true code-block-bg: true code-block-border-left: "#3498db" code-block-font-size: 0.95em code-block-border-radius: 6px code-block-padding: 1em code-block-hashpipe: trueexecute: echo: true warning: false eval: true freeze: auto cache: false # Disable MathJax if not using math # (prevents 404s for MathMenu.js, MathZoom.js) enabled: true html-math-method: none---# IntroductionThis report provides a comprehensive analysis of <strong>global child mortality rates</strong> for ages 5-9, using recent <strong>UNICEF data</strong>. Through a series of <strong>visualizations</strong>, we explore how mortality rates have <strong>changed over time</strong>, how they <strong>differ by country and region</strong>, and what <strong>factors</strong> may contribute to these disparities. The report also examines the <strong>relationship between economic development and child mortality</strong>, as well as <strong>differences by gender</strong>, to highlight areas where <strong>targeted interventions</strong> are most needed.# Key Findings- <strong>Child mortality rates</strong> show substantial <strong>variation across countries and regions</strong>, with some countries experiencing rates several times higher than others.- While <strong>global mortality rates have declined</strong> over recent decades, progress has been <strong>uneven</strong>, and significant <strong>disparities persist</strong>, especially in <strong>low-income regions</strong>.- <strong>Socioeconomic status</strong>, <strong>healthcare access</strong>, and <strong>education</strong> are strongly associated with lower child mortality rates, as shown by the <strong>negative correlation between GDP per capita and mortality</strong>.- <strong>Gender differences</strong> in child mortality are <strong>minimal at the global level</strong>, but continued monitoring is important to ensure <strong>equity</strong>.# Trend Analysis```{python}import pandas as pdimport plotly.express as pximport numpy as npfrom sklearn.linear_model import LinearRegression# Load datachild = pd.read_csv('unicef_indicator_1.csv')meta = pd.read_csv('unicef_metadata.csv')# Get the most recent GDP per capita for each countrymeta_gdp = meta.dropna(subset=["GDP per capita (constant 2015 US$)"])meta_gdp = meta_gdp.sort_values("year").groupby("country", as_index=False).last()# Filter for 2022, totalchild_2022 = child[(child['time_period'] ==2022) & (child['sex'] =='Total')]# Merge with most recent GDP per capitamerged = pd.merge(child_2022, meta_gdp[['country', 'GDP per capita (constant 2015 US$)']], on='country', how='left')merged = merged.dropna(subset=['GDP per capita (constant 2015 US$)', 'obs_value'])# Remove zero or negative GDP valuesmerged = merged[merged['GDP per capita (constant 2015 US$)'] >0]# Take log of GDP per capitamerged['log_gdp'] = np.log10(merged['GDP per capita (constant 2015 US$)'])# Prepare regressionX = merged['log_gdp'].values.reshape(-1, 1)y = merged['obs_value'].valuesmodel = LinearRegression()model.fit(X, y)y_pred = model.predict(X)# Scatter plot with regression linefig_gdp = px.scatter( merged, x='log_gdp', y='obs_value', hover_name='country', title='Child Mortality Rates by Log(GDP per Capita) (2022)', labels={'log_gdp': 'Log10(Most Recent GDP per Capita, constant 2015 US$)','obs_value': 'Mortality Rate (per 1000 children)' })# Add regression linefig_gdp.add_traces(px.line( x=merged['log_gdp'], y=y_pred, labels={'x': 'Log10(Most Recent GDP per Capita, constant 2015 US$)', 'y': 'Predicted Mortality Rate'}).data)fig_gdp.update_traces(line=dict(color='red'), selector=dict(type='scatter', mode='lines'))fig_gdp.update_layout( showlegend=False, height=500, margin=dict(l=40, r=40, t=60, b=60), plot_bgcolor='white', paper_bgcolor='white')fig_gdp.show()```**Analysis:**The scatterplot demonstrates a clear <strong>negative correlation</strong> between a country's <strong>economic prosperity</strong> (as measured by <strong>GDP per capita</strong>) and its <strong>child mortality rate</strong>. Countries with <strong>higher GDP per capita</strong> tend to have much <strong>lower mortality rates</strong>, indicating that <strong>economic development</strong> is a key factor in improving child health outcomes. The <strong>log scale</strong> on the x-axis highlights that the greatest reductions in mortality are seen as countries move from <strong>low to middle income</strong>. However, some countries <strong>deviate from this trend</strong>, suggesting that other factors, such as <strong>healthcare quality</strong>, <strong>conflict</strong>, or <strong>policy</strong>, also play important roles. This visualization underscores the importance of both <strong>economic growth</strong> and <strong>targeted health interventions</strong>.# Time Series Analysis```{python}import pandas as pdimport plotly.express as pxdf = pd.read_csv('unicef_indicator_1.csv')trend = df[df['sex'] =='Total'].groupby('time_period')['obs_value'].mean().reset_index()fig_time = px.line( trend, x='time_period', y='obs_value', title="Global Mortality Rate Trend Over Time", labels={"obs_value": "Average Mortality Rate (per 1000 children)", "time_period": "Year"}, markers=True)fig_time.update_traces(line=dict(width=3, color='#4F6D7A'))fig_time.update_layout( height=500, margin=dict(l=40, r=40, t=60, b=60), plot_bgcolor='white', paper_bgcolor='white')fig_time.show()```**Analysis:**The time series line chart reveals a <strong>steady and substantial decline</strong> in <strong>global child mortality rates</strong> for ages 5-9 over the past several decades. This <strong>downward trend</strong> reflects major improvements in <strong>public health</strong>, <strong>nutrition</strong>, <strong>disease prevention</strong>, and <strong>access to medical care</strong> worldwide. While the overall pattern is positive, the chart also shows <strong>periods of slower progress</strong> and occasional <strong>plateaus</strong>, which may correspond to <strong>global crises</strong> or <strong>regional setbacks</strong>. <strong>Sustaining and accelerating this progress</strong> will require continued investment and attention, especially in countries where declines have stalled.# Global and Regional Comparison of Averages of Child Mortality Rates```{python}import pandas as pdimport plotly.express as pxdf = pd.read_csv('unicef_indicator_1.csv')latest_year = df.groupby('country')['time_period'].max().reset_index()df_latest = pd.merge(df, latest_year, on=['country', 'time_period'])df_latest = df_latest[df_latest['sex'] =='Total']fig_map = px.choropleth( df_latest, locations="country", locationmode="country names", color="obs_value", hover_name="country", color_continuous_scale="Reds", title="Most Recent Global Child Mortality Rate by Country", labels={"obs_value": "Mortality Rate (per 1000 children)"}, height=520)fig_map.update_layout( coloraxis_colorbar=dict( orientation='h', x=0.5, xanchor='center', y=-0.18,len=0.7, thickness=16, title_side='top', title_font_size=14, tickfont_size=12 ), margin=dict(l=10, r=10, t=60, b=60), plot_bgcolor='white', paper_bgcolor='white')fig_map.show()```**Analysis:**The world map visualization highlights <strong>stark geographic disparities</strong> in <strong>child mortality rates</strong>. <strong>Sub-Saharan African countries</strong> are shown in the darkest shades, indicating the <strong>highest mortality rates</strong>, while countries in <strong>Europe</strong>, <strong>North America</strong>, and parts of <strong>Asia</strong> have the <strong>lowest rates</strong>. This pattern reflects differences in <strong>healthcare infrastructure</strong>, <strong>economic development</strong>, and <strong>social stability</strong>. The map also reveals that some countries have made <strong>significant progress</strong>, while others continue to face <strong>persistent challenges</strong>. These findings emphasize the need for <strong>region-specific strategies</strong> and <strong>international support</strong> to address the most affected areas.# Gender Disparities in Averages of Global Child Mortality Rate```{python}import pandas as pdimport plotly.express as px# Load datachild = pd.read_csv('unicef_indicator_1.csv')latest = child[child['time_period'] ==2022]# Only keep Male and Femaleavg_by_gender = latest[latest['sex'].isin(['Male', 'Female'])].groupby('sex', as_index=False)['obs_value'].mean()# Gender-oriented color palettegender_colors = ['#1f77b4', '#e377c2'] # blue for Male, pink for Femalefig_gender = px.bar( avg_by_gender, x='sex', y='obs_value', color='sex', text=avg_by_gender['obs_value'].round(2), title='Child Mortality Rates by Gender (2022)', labels={'obs_value': 'Average Mortality Rate (per 1000 children)', 'sex': 'Gender'}, color_discrete_sequence=gender_colors)fig_gender.update_traces(textposition='outside')fig_gender.update_layout( showlegend=False, height=500, margin=dict(l=40, r=40, t=60, b=60), plot_bgcolor='white', paper_bgcolor='white')fig_gender.show()```**Analysis:**The bar chart compares <strong>average child mortality rates</strong> for <strong>boys and girls</strong> in 2022. The results show that the rates are <strong>nearly identical</strong>, with only a very <strong>slight difference between genders</strong>. This suggests that, at the <strong>global level</strong>, <strong>gender-based disparities</strong> in child mortality for ages 5-9 are <strong>minimal</strong>. However, it is important to note that this global average may mask <strong>differences within specific countries or regions</strong>, where <strong>cultural, social, or economic factors</strong> could lead to greater disparities. <strong>Ongoing monitoring</strong> and <strong>disaggregated data</strong> are essential to ensure that both boys and girls have <strong>equal opportunities for survival and health</strong>.# ConclusionThis report demonstrates that while <strong>global child mortality rates have declined</strong>, significant <strong>disparities remain</strong> across <strong>countries, regions, and genders</strong>. The visualizations underscore the importance of <strong>economic development</strong>, <strong>healthcare access</strong>, and <strong>targeted interventions</strong> in reducing mortality. <strong>Continued investment</strong> in healthcare, education, and <strong>region-specific policies</strong> is essential to ensure all children have the opportunity to <strong>survive and thrive</strong>, regardless of where they are born or their gender.# References- UNICEF. (2022). Global Mortality Rates Ages 5-9 for 2022. Retrieved from UNICEF website- World Health Organization (WHO). (2022). Child Mortality. Retrieved from WHO website- United Nations Children's Fund (UNICEF). (2022). The State of the World's Children 2022. Retrieved from UNICEF website- United Nations Development Programme (UNDP). (2022). Human Development Report 2022. Retrieved from UNDP website- World Bank. (2022). World Development Indicators. Retrieved from World Bank website- UNICEF. (2022). Child Mortality: A Global Perspective. Retrieved from UNICEF website